Arielle S. Keller

Prior research linking resting-state networks (RSNs) to adolescent depression has yielded inconsistent findings, partly because group-average templates poorly capture individual brains; a mismatch that may be especially consequential during adolescence, when RSNs grow increasingly heterogeneous across individuals. Using a personalized analysis pipeline in the longitudinal Characterizing Adolescent Trajectories of Depression cohort (38 depressed adolescents [28 female]; ages 14–18; 181 usable scans; avg. participation: 446.18 days), we derived individualized definitions of three canonical depression-related RSNs. Because longitudinal RSN connectivity may reflect both trait-like vulnerability and state-like symptom fluctuation, we used mixed-effects models to decompose connectivity profiles into both components. FPN connectivity showed a significant state-trait dissociation: chronically elevated connectivity predicted greater average symptom burden, while transient increases predicted momentary relief. These effects were undetectable using group-atlas estimates. We suggest FPN connectivity plays a dual, compensatory role in adolescent depression and demonstrate that personalized definitions can reveal clinically meaningful brain–symptom associations.

The Adolescent Brain Cognitive Development (ABCD) Study is the largest U.S.-based neuroimaging initiative of adolescent brain maturation. Diffusion MRI (dMRI) provides unique insights into white matter organization, yet applying advanced processing pipelines and managing technical variability across scanning environments remains challenging at scale. To address these issues, we present ABCD-BIDS Community Collection (ABCC) release 3.1.0, including a curated resource of more than 24,000 fully processed ABCD dMRI datasets. ABCC provides fully processed images, nuanced image quality metrics, advanced microstructural measures, and person-specific bundle tractography. Evaluating these rich data revealed that measures of diffusion restriction and non-Gaussianity—in particular the intracellular volume fraction from NODDI and return-to-origin probability from MAP-MRI—were highly sensitive to neurodevelopment and robust to variation in image quality. Additionally, harmonization of microstructural features markedly improved the cross-vendor generalizability of developmental effects. Together, ABCC accelerates reproducible, rigorous research on adolescent white matter development.

Purpose: Children’s reading development is shaped through a complex interplay of genetic and environmental influences within families. This exploratory study investigated the intergenerational transmission of reading achievement within biological families. Prior studies have often measured different constructs in parents and children; many have included only mothers in models; and few have examined whether transmission varies by parent and child sex. We systematically examined whether parent-child associations in reading achievement varied by reading subcomponent, parent sex, and child sex. Method: Participants included 80 families (mother, father, child[ren] aged 7–12 years; n = 267). All participants completed identical standardized assessments across four reading subcomponents. Sex-stratified linear mixed models were used to estimate associations by parent and child sex, with shared reading explored as a mediator. Results: Sex-discordant dyads (father-daughter, mother-son) exhibited statistically significant associations across passage comprehension, reading fluency, and phonemic decoding efficiency, with sight word reading efficiency showing consistent trends but non-significant associations. Direct coefficients in significant sex-discordant dyads ranged from b = 0.49 to b = 0.89 (standard score units), with 99-100% sign stability under Monte Carlo cross-validation. Sex-concordant dyads showed non-significant associations. Permutation-based contrasts indicated that differences between sex-concordant and sex-discordant dyads were significant for passage comprehension, with suggestive evidence (confidence intervals excluding 0) for phonemic decoding. Shared reading did not significantly mediate associations. Conclusion: Observed associations were numerically stronger between children and opposite-sex parents, challenging assumptions of uniform transmission by dyad type. Passage comprehension and sentence reading fluency showed strongest similarity, while associations for sight word reading were weak. These exploratory findings underscore the importance of measure selection and the consideration of sex as a biological variable in familial control research.

Sustained attention is an important neurobiological process impaired in multiple neurodevelopmental disorders, including attention-deficit/hyperactivity disorder (ADHD) and autism. Connectome-based predictive models (CPMs) of sustained attention function and autism symptoms generalize across samples, but whether their features are shared or phenotype-specific is unknown. We tested within-phenotype consistency and across-phenotype specificity in 5 published CPMs predicting autism symptoms or sustained attention performance in youth and adults, equating model size (top 1,000 positively and negatively predictive edges per model). No edge or network pair was shared by all 5 models. Same-phenotype models were nonetheless consistent: the whole-brain pattern of edge-behavior associations correlated in all 4 same-phenotype dataset pairs (r > 0.13, P < 0.005). Features were also largely phenotype-specific: only 1 of 6 cross-phenotype pairs correlated significantly (negatively), and 2 models built to predict autism symptoms vs. attention in the same dataset shared no more edges than chance. Finally, representational similarity analysis revealed that scan type (rest vs. task), participant age, and autism diagnosis did not explain similarity in edge-behavior associations across datasets. Together, these results suggest that feature sets are reproducible within, but distinct across, phenotypes, consistent with autism symptoms and sustained attention being related but separable. Highlights: We interrogated five previously published, transdiagnostic models of sustained attention and autistic phenotypes Functional connectome-based models shared edges and networks when phenotypes were similar Models predicting attention phenotypes shared more similar features with each other than models predicting autism symptoms and vice-versa Age, functional run type, and diagnosis did not affect model similarity

Background: Attention problems are common transdiagnostic symptoms of psychiatric illness. Although environmental exposures and experiences influence attention during adolescent development, the underlying neural pathways by which they do so is unclear. Methods: We measured attention problems, attention-related brain networks, and multidimensional environmental experiences (the “exposome”) using data from the ABCD Study (N = 11,878). We tested whether the exposome is associated with 9-10-year-olds’ attention-related brain network strength and current and future attention problems. We further examined cross-sectional indirect pathways linking the exposome, brain network strength, and attention problems. Results: The exposome predicted youths’ current and future self-, caregiver-, and teacher-reported attention problems as well as their current attention-related brain network strength. This brain network signature of sustained attention also predicted attention problems from all three reporters. Indirect effects models revealed that the exposome was associated with current reported attention problems both directly and indirectly though this brain signature. Conversely, predictive brain network strength was related to attention problems both directly and indirectly through the exposome. Conclusion: Interactions between environmental exposures, experiences, and brain network organization are associated with attention problems in early adolescence. These findings support a bidirectional framework linking the environment and functional brain networks in the development of attention problems.

Despite decades of neuroimaging research, how white matter develops along the length of major tracts in humans remains unknown. Here, we identify fundamental patterns of white matter maturation by examining developmental variation along major, long-range cortico-cortical tracts in youth ages 5-23 years using diffusion MRI from three large-scale, cross-sectional datasets (total N = 2716). Across datasets, we delineate two replicable axes of human white matter development. First, we find a deep-to-superficial axis, in which superficial tract regions near the cortical surface exhibit greater age-related change than deep tract regions. Second, we demonstrate that the development of superficial tract regions aligns with the cortical hierarchy defined by the sensorimotor-association axis, with tract ends adjacent to sensorimotor cortices maturing earlier than those adjacent to association cortices. These results reveal developmental variation along tracts that conventional tract-average analyses have previously obscured, challenging the implicit assumption that white matter tracts mature uniformly along their length. Such developmental variation along tracts may have functional implications, including mitigating ephaptic coupling in densely packed deep tract regions and tuning neural synchrony through hierarchical development in superficial tract regions – ultimately refining neural transmission in youth.